has any one got a poisson and/or students T distribution i can borrow [ i need the source unfortunately as im going to have to hack it probably to do a least square approx ]Heres why :-im trying to find a best fit for a skewed set of data .. as the data set gets larger i suppose i could use a normal distribution but being limited to 32 bit im already hitting the 2 gig limit of the O/S so id rather use some thing better tuned to the data Its true i am being a bit lazy by asking but i very rarely use the floating point stuff and it would therefore take a while to do Being a tad old now i dont want to die trying to get one running ...regards mike burr

Dear HSEi have to say im not that confident at handling all the marvelous stuff Raymond has provided. The computation of probability is relatively simple but the fitting it to the data is not Example data set [3,2];[2,5];[4,6,1];[2,11,2];[4,13,5];[3,17,10]'[4,22,15,1];[2,27,25,2] ........[2,435,10759,86440,284674,411687,258279,64131,5033,65],[4,544,11593,92883,316709,476204,312411,82577,7121,110],....where the length of sequence extends according to a rule ive established as 1+ [n(n+1)/2] The data you'll notice has a very Poisson like distribution BUT the k and lambda are not discrete so in order to match the distribution to the data it will be necessary to vary k and lambda to match the profile I suppose people might have wondered reading the first post what i was doing as Poisson is not immediately obvious as a best fit solution !!! i hope this sort of explains what im thinking of doing in trying to establish a connection between non linear and discrete functions this way regards mike b

@@jj thanks very much jj i will need data for testing the distribution is correct so thats brilliant thank you Ill be running the distribution using a least squares system to try and align it to the data i put on the post [ which is both invariant and i think is independant .. if not then we've all got to rethink number theory !!!!!] @@hse what a brilliant facility QWORD has provided on source forge ... [wasnt sure whether i should put the link up or not as it took a quick search to find everything ]though im glad i only recently joined as i might have used the very comprehensive linked list whereas i developed a file system pertinent to each of the programs ive developed and this has not ony furthered my understanding of the computer os and hardware but acts as a foundation for further development [trade off between a better easier system to use and something you understand because you had to make it ] regards mike b

The main part of the program sets up a Levenberg-Marquardt solver and some simulated random data. The data uses the known parameters (5.0,0.1,1.0) combined with Gaussian noise (standard deviation = 0.1) over a range of 40 timesteps. The initial guess for the parameters is chosen as (0.0, 1.0, 0.0).

Most of the time, I use my own Least Mean Square method because is very easy to make it in assembly. For Levenberg-Marquardt I use R-project wich is a good scientific reference (also free). (I don't know if Gunther worked in that project).

thanks very much for your kind assistance and recommendations .. i should have looked at the things id done here before as it was pretty much glaringly obvious .. anyway the answer is a gamma like distribution of the form [ (t!)^2 (t+1)] / [((t-k)!)^2 * (k!)*2 * (k+1)] which prob sounds a bit elaborate but wasnt too hard to find .. I think i now have the solution to a thing called Ramseys Theorem .Paul Erdos made an exagerated quote as to the difficulty of computing R(5) and thence R(6) which is quite amusing regards mike b